This renewal application will combine statistical analyses of extraordinary datasets with mathematical models and optimization techniques to design epidemic control strategies in both resource-rich and resource- constrained countries. In addition, strategies for optimizing the expansion of treatment programs will be designed. The recent development of antiretroviral-based tools to prevent HIV transmission could potentially alter the course of the HIV pandemic. At the same time, the rollout of ARV treatment in Africa is expanding dramatically. Uncertainty about the number of individuals who are undiagnosed and infected with HIV (i.e., the size of the hidden epidemic) is a critical obstacle to the success o both treatment programs and interventions. We will estimate the size of the hidden epidemic in Denmark (in Copenhagen), Botswana, and Lesotho, through analyses of data provided by collaborators in these countries. These estimates will form the foundation for our analyses. In Aim 1A we evaluate, for the Danish community of men who have sex with men, whether increasing levels of treatment have been correlated with decreasing HIV incidence; in Aim 1B we use modeling to determine the conditions that need to be met for treatment as prevention-based interventions to eliminate HIV in that community. In Aim 2 we use georeferenced data to construct country-level predictive HIV prevalence maps for, and estimate the burden of disease in, Botswana and Lesotho; two countries with among the most severe HIV epidemics worldwide. We use these maps to quantify the accessibility to treatment across each country and to determine how to optimize the efficiency of treatment programs (e.g., minimize interruptions in the treatment supply chain). In Aim 3, using the results from Aim 2, we construct transmission models and determine how to optimize the design of interventions to reduce HIV transmission; specifically to determine how best to minimize incidence, under resource constraints.